Top

Published in:

2020 | OriginalPaper | Chapter

Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks

Authors : Paweł Ksieniewicz, Róża Goścień, Mirosław Klinkowski, Krzysztof Walkowiak

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The following paper considers pattern recognition-aided optimization of complex and relevant problem related to optical networks. For that problem, we propose a four-step dedicated optimization approach that makes use, among others, of a regression method. The main focus of that study is put on the construction of efficient regression model and its application for the initial optimization problem. We therefore perform extensive experiments using realistic network assumptions and then draw conclusions regarding efficient approach configuration. According to the results, the approach performs best using multi-layer perceptron regressor, whose prediction ability was the highest among all tested methods.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter On the Impact of Network Data Balancing in Cybersecurity Applications

next chapter Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network

https://github.com/w4k2/regression-aided-optimization.

Awaji, Y., et al.: High-capacity transmission over multi-core fibers. Opt. Fiber Technol. 35, 100–107 (2017)CrossRef

Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 1–99 (2018). https://doi.org/10.1186/s13174-018-0087-2CrossRef

CISCO: Cisco Visual Networking Index: Forecast and Trends, 2017–2022. Technical report (2019)

Gerstel, O., Jinno, M., Lord, A., Yoo, S.J.B.: Elastic optical networking: a new dawn for the optical layer? IEEE Comm. Mag. 50(2), 12–20 (2012)CrossRef

Goścień, R.: On the efficient dynamic routing in spectrally-spatially flexible optical networks. In: 2019 11th International Workshop on Resilient Networks Design and Modeling (RNDM), pp. 1–8, October 2019

Goścień, R., Lechowicz, P.: On the complexity of RSSA of any cast demands in spectrally-spatially flexible optical networks. In: 9th International Network Optimization Conference Avignon, France, 12–14 June 2019, p. 7 (2019)

Klinkowski, M., Ksieniewicz, P., Jaworski, M., Zalewski, G., Walkowiak, K.: Machine learning assisted optimization of dynamic crosstalk-aware spectrally-spatially flexible optical networks. J. Lightwave Technol. 1 (2020). https://doi.org/10.1109/JLT.2020.2967087

Klinkowski, M., Lechowicz, P., Walkowiak, K.: Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking. Opt. Switch. and Netw. 27, 58–78 (2018)CrossRef

Ksieniewicz, P., Woźniak, M., Cyganek, B., Kasprzak, A., Walkowiak, K.: Data stream classification using active learned neural networks. Neurocomputing 353, 74–82 (2019)CrossRef

10.

Mata, J., et al.: Artificial intelligence (AI) methods in optical networks: a comprehensive survey. Opt. Switch. and Netw. 28, 43–57 (2018). https://doi.org/10.1016/j.osn.2017.12.006CrossRef

11.

Musumeci, F., et al.: An overview on application of machine learning techniques in optical networks. IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2018)CrossRef

12.

Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH

13.

Rafique, D., Velasco, L.: Machine learning for network automation: overview, architecture, and applications [invited tutorial]. IEEE/OSA J. Opt. Commun. Netw. 10(10), D126–D143 (2018). https://doi.org/10.1364/JOCN.10.00D126CrossRef

14.

Saridis, G.M., Alexandropoulos, D., Zervas, G., Simeonidou, D.: Survey and evaluation of space division multiplexing: from technologies to optical networks. IEEE Commun. Surv. Tutorials 17(4), 2136–2156 (2015)CrossRef

15.

Walkowiak, K.: Modeling and Optimization of Cloud-Ready and Content-Oriented Networks. SSDC, vol. 56. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30309-3CrossRefMATH

16.

Zyblewski, P., Ksieniewicz, P., Woźniak, M.: Classifier selection for highly imbalanced data streams with Minority Driven Ensemble. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2019. LNCS (LNAI), vol. 11508, pp. 626–635. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20912-4_57CrossRef

Title: Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks
Authors: Paweł Ksieniewicz
Róża Goścień
Mirosław Klinkowski
Krzysztof Walkowiak
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2020
Print ISBN: 978-3-030-50422-9

Electronic ISBN: 978-3-030-50423-6

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-50423-6_16

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner